Using Data to Generate Evidence Which Drives Value

Session SC3, February 11, 2019

James E. Tcheng, MD

Professor of Medicine, Professor of Informatics

Duke University Health System, Durham, NC

James E. Tcheng, MD

• Fees for non-CME services received directly from a commercial

interest or their agents: GE Healthcare

• Ownership interest (stocks, stock options or other ownership

interest excluding diversified mutual funds): International

Guidelines Central, LivMor

• Data Safety Monitoring Board: AstraZeneca

Relationships with Industry

• Establish the strategic importance of using data captured across

the point of use and its ability to solve the cost/quality equation

• Identify ways to disseminate data so that it guides and informs

clinical practice

• Review the technologies needed to create, maintain, and support

robust data capture

• Examine the challenge and opportunities with data governance

across enterprises

Learning Objectives

• At the intersection of cost, quality and outcomes:

– Aligned hospital administration, supply chain, physicians

– Data, data, data

• Given the strategic importance of data

– Challenge 1: high quality data

– Challenge 2: workflow integrated data capture at point of use

• Complex systems theory

• Data management logistics

– What are the goals?

– What data is needed?

– Why not wait for “interoperability” to solve this?

– What is best practice for capture of high quality data?

• Who are external drivers?

– FDA (UDI), NESTcc

Topics

“Chronic diseases can be studied, but

not by the methods of the past. If one

wishes to create useful data …

computer technology must be

exploited.”

— Eugene Stead, MD (ca. 1965)

Data and Health Research

A New Concept in Medicine?

• Led to the concept of the “computerized

textbook of medicine”

• Foundation of the Duke Databank for

Cardiovascular Diseases (DCRI)

• Spurred a generation of clinical and

quantitative researchers

Is Healthcare Changing for the Better …

Envisioned Reality

EHR “Meaningful Use” EHR meaningless burden

Usability and productivity Death by clicking

Patient engagement AVS drivel

Effective clinical care CDS trivial pursuit

Population health Resource consumption focus

Bending healthcare cost curve Cost control and penalties

Better provider work life NOT!

Torrent of real-world data Puddles of document exchange

Big (clinical) data analytics Small transactions data

Leveraged RCTs via registries 20

century paradigms

Aspiration

If you don’t know where you are going,

chances are you will end up somewhere else.

Yogi Berra

If you don’t know where you are going,

any road will take you there.

Lewis Carroll

Is Healthcare Changing for the Better …

What Should the Common Denominator Be?

Clinical documentation

Registry reporting

Quality and performance

Supply chain

Device safety, surveillance

Clinical research

RWD  RWE

Computational constructs:

CDS, AI, machine learning, etc.

The Importance of Data Quality

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D. Fridsma, 2018

Swivel Chair Interoperability

W. Rishel

Documents in

Clinical Systems

Registry Data Entry

(and any other

requirement for data)

American College of Cardiology -

National Cardiovascular Data Registry:

CathPCI Operational Environment

• >90% US PCI procedures, process and quality focus

• Relies on clinical documentation and “swivel chair”

interoperability (i.e., little structured reporting)

• NCDR data dictionary: clinical / operational focus, not data

standards (primarily name of concept, allowed values)

• No authoritative process to follow for developing data

elements as HIT standards (& no single repository)

• No consistent instrument for technical consumers (HIT

vendors, db developers): data elements, data capture

instruments, workflow engineering

• $10 million+ book of business  $500 million+ site

expenditure (2000 sites, 2+ FTEs per site)

Complex systems

theory is a combination

of linear, nonlinear,

dynamic, self-

organizing, evolutionary,

diverse, and uniform

relationships, which

makes for a messy

universe …

Key Healthcare Targets

• Clinical care: data as a medical partner

X Clinical documentation focused on care (not billing)

X Engineered data capture via care processes

X Outcomes-based (not just systems / processes)

X Incorporate patient-reported information, f/u

X Clinical decision support, pre-authorization support,

reduction in variability, revenue recovery

• Research: changing the RCT paradigm

X Observational / CE studies, pragmatic trials

• Regulatory: changing the evidence paradigm

X RWD  RWE (not just adverse events / recalls)

X Pharmacovigilance, device surveillance

Excellence & Mediocrity

“A society which scorns

excellence in plumbing simply

because it is plumbing, but

rewards mediocre philosophy

simply because it is philosophy,

will soon become a society in

which neither its pipes nor its

theories will hold water.”

John W. Gardner (1961)

HEW Secretary under Lyndon Johnson

Engineer of the Great Society (Medicare, PBS)

Exchange, Use, and Reuse of Data

Requires Shared Data Definitions,

Including Semantics

What is Required to Share Data?

• What: conceptual clinical definitions (vocabulary)

• When, where: interfaces (forms) to capture specific data

at point of care by individuals closest to the data

• How:

– physical layer (data capture and storage, native

data interoperability)

– communication layer (syntactic, semantic

interoperability)

– common / standard format (data modeling)

• bindings to controlled terminologies (SNOMED-

CT, LOINC, RxNorm)

Data Destinations: Multiple Masters

 Clinical care, documentation

 Payers

 Quality assessment, registries, CRNs

 Patients

 Health system / population health

 Research

 FDA, surveillance reporting

 Federal, state reporting programs

 Oh yes … clinicians

Recipients

Producers

… who are time-challenged, short-staffed, over-burdened …

HIT /

EHR

(POC

form)

Discrete

Data

(CDEs)

Structured

Documentation

DQR

Credible

Data

Analysis,

Measures

Benchmark

Registries

Active

Quality

Improvement

Cycle

Duke Heart Center - Dataflow End State

Heart

Data

Mart

Research

Build infrastructure

Use the data

Near Real Time Clean Up

Structured

Reporting

Concurrent Data Acquisition Model

and Data Flow Into Registries

Clinical data collected during case

Registry data

mapping rules

Medical

History event

Cath/PVI/EP/CT

Surgery report

Registry

submission

 Type I diabetes

 Type II diabetes

Type I Diabetes If Type I = Yes

then Diabetes

= Yes

Diabetes = Yes

Unidirectional carry

forward (CF)

with CF button trigger N+1

Unidirectional CF

with automated CF, plus

“Lock/Unlock” data, plus ad

hoc CF button trigger

Cath Lab / Surgery Staff

Registry Nurse

Frequent / real time, accurate, missingness reports,

real time metrics, real time risk calculation

• Data quality

• Performance improvement

What is Structured Reporting?

• Explicit clinical data captured by the person closest to that

data, integrated into clinical workflow (e.g. MA, tech, RN, pt)

• Informatics formalisms: universal, well-defined common data

elements; data model that parallels (i.e., is representational

of) clinical care model

• Data compiled by the computer to produce majority of report

content; MD validates the data, contributes only cognitive

assessment and recommendations

• Output: the structured report

• ROI:  data quality /quantity,  redundancy / repetition, time to

final reports, FTE requirements  augmented knowledge,

revenue recovery, financial gains

How Is Structured Reporting Done?

• Engineered, best-practice workflows

• Just in time, context specific, high usability,

point of care data capture

• Data persistence

• Lots of business rules

• Optimized IT form factors

In other words …

• Command of who does what when, where,

and how

Critical

Step

Device (UDI) + Clinical Data Integration

Usage DFT

Pt data

Accession #

UDI (DI + PI)

Serial, lot #

Expiration

Procedure

data

Pyxis

Procedure

Stations

BD CCE

Care

Coordination

Engine

Lumedx

Procedure

Documentation

Epic

Radiant

EHR

SAP Replenishment

JIT Ordering

SAP Item Master

Orders ORM

Pt data

Accession, case #

Procedure data

Items data (ASCII)

Inventory replenishment data

What Does UDI Enable?

• Standardized device description  procedure report

– no more MD recall, transcription errors

• Inventory management (lower PAR levels  >$1m

savings / yr - Mercy Cath Lab)

• Device use attribution (e.g., waste, failure to deploy)

• Consignment device management

• UDI to the EHR (exported to the UDI device table)

• Device explants (e.g., CIED) – closing the loop

• Administrative reporting (e.g., device usage reports)

• Adverse event reporting (e.g., FDA MedWatch)

How Important is it to Identify an Implant?

Consumers - Decisions Within Days

Registry Assessment of Peripheral

Interventional Devices (RAPID)

• The MDEpiNet RAPID project is designed to advance

the foundational elements of the approach for the

evaluation of medical devices used to treat and

manage peripheral artery disease.

• RAPID is an archetype of the total product

lifecycle (TPLC) ecosystem.

• It is one of a series of projects initiated to advance

and demonstrate the interoperable flow of data

across electronic health information systems.

• Is fundamental to the basis of the development of the National

Evaluation System for Health Technology (NEST).

• A demonstration project of MDIC/NESTcc, a public-private

partnership.

GUDID/Informatics Workgroup Phase III

1.Objective

a) To improve UDI capture/utilization and broaden its impact

b) Demonstration Project

Use findings and partnerships built in Phase I and Phase II to

form a partner-based quality improvement study of UDI workflow

Develop process to capture RWE from selected data partners for

worldwide regulatory support of device TPLC

2.Methodology

– Clarifying structured data device parameters to be assigned by

manufacturers to improve quality of clinically relevant size and device

categorization in GUDID

– Assessing existing workflows at NESTcc data partners who are

committed and show high level of UDI adoption maturity

UDI in RAPID:

Improve Decision Making with Better UDI Data

Methodology (cont’d)

– Assisting implementation of the core RAPID clinical data elements

(including UDI) into EHR or other point of care systems

– Exploring mechanism for transfer of data into a PAD registry

– Evaluating impact on data partner workflows and reductions in data

capture, data transfer, and feedback to improve value of UDI to multi-

stakeholders across the PAD lifecycle

• Expected NEST impact: facilitate development of UDI workflow processes

in NEST partners that could be leveraged by other healthcare systems, as

well as evidence generation processes that could be utilized across the

medical device industry

UDI in RAPID:

Improve Decision Making with Better UDI Data

• All segments must work in concert toward common goal:

data driving the cost, quality and outcomes equation

• Manufacturers: discrete data

• Supply chain: integration into clinical care

• HIT: solutions enabling engineered, best practice

clinical workflows

• FDA: UDI, supplemental device attributes

• Clinical: structured reporting

• Professional societies: leadership, coordination

• Healthcare systems: vision, implementation

• Healthcare: data interoperability, not documents

• Contact: james.tcheng@duke.edu

Summary